Four decades of molecular theory and computation have helped form the modern understanding of the physical chemistry of organic semiconductors. Whereas these efforts have historically centered around characterizations of electronic structure at the single-molecule or dimer scale, emerging trends in noncrystalline molecular and polymeric semiconductors are motivating the need for modeling techniques capable of morphological and electronic structure predictions at the mesoscale. Provided the challenges associated with these prediction tasks, the community has begun to evolve a computational toolkit for organic semiconductors incorporating techniques from the fields of soft matter, coarse-graining, and machine learning. Here, we highlight recent advances in coarse-grained methodologies aimed at the multiscale characterization of noncrystalline organic semiconductors. As organic semiconductor performance is dependent on the interplay of mesoscale morphology and molecular electronic structure, specific emphasis is placed on coarse-grained modeling approaches capable of both structural and electronic predictions without recourse to all-atom representations.