Modeling thermodynamic properties can be challenging when the data availability for parameters identification is limited. Fully predictive group contribution (GC) methods have been developed as an alternative to overcome data scarcity. However, in order to provide a higher degree of accuracy, most recent GC approaches require detailed information on the molecule's structure, which is not acquirable for systems with unspecified components. This work intends to establish the foundation to overcome this limitation. The proposed PC-SAFT approach assisted by a homosegmented group contribution scheme permits parameters calculation with accuracy due to one adjustable parameter without requiring meticulous information regarding the molecular structure, for instance, the relative position between carbon-centered groups. This semipredictive approach is especially suitable for cases in which some data are available but are not taken into consideration by current GC models. A genetic algorithm-based routine was developed to determine the group contribution parameters by simultaneously optimizing vapor pressure and saturated liquid density calculations of sixty-nine pure hydrocarbons. Further analysis indicated good predictive capabilities of the model in the extreme case where a single vapor pressure data point was provided to adjust the model parameter, with an average percentage absolute deviation of 3.79% in saturation pressure and 2.40% in saturated liquid density over 18 additional compounds that were not included in the training database. Overall, the results have shown that satisfactory predictions are possible provided a simple quantification of different carbon groups based on the type of bonds they form and a single saturation pressure point. Therefore, given that the proposed approach does not require the relative position between groups in a molecule, the method may extend the applicability of the group contribution concept to, some specific industry applications.