Determination of dry pressure drops is often the preliminary diagnostic tool for characterizing structured packing-containing columns. One conventional approach that ushered in this area evolves around the use of Ergun expressions along with mandatory experimental pressure drops for the fitting of some empirical constants characterizing a given packing. This method is strictly representational, and incapable of predicting the impact on bed pressure drop of changes in packing geometry, e.g., corrugation angle, channel size, or packing topography. In this work, a combined mesoscale-microscale predictive approach was developed to apprehend the aerodynamic macroscale phenomena in structured packings. The proposed method consists in identifying recurrent mesoscale patterns (the representative elementary units, REU) wherein the constitutive microscale dissipation mechanisms occur. The dissipative phenomena that were identified to be important are: the elbow loss and jet splitting at the packed bed entrance, the elbow loss at the column wall, the elbow loss at the jump from one layer to another, and the collisional losses at the criss-crossing junctions. Each mechanism was simulated over a wide Reynolds range spanning the pure creeping flow to the fully developed turbulent flow using three-dimensional computational fluid dynamics (CFD). Postulating additiveness of dissipation, the overall pressure drop was reconstructed. The approach was validated using experimental dry pressure drop data for five packing types (Flexipac, Gempak, Mellapak, Sulzer BX and Montz-Pak) having different channel sizes, corrugation angles, and surface topography. Our goal was to advocate CFD as a quicker and cheaper means for design and optimization, in terms of energy dissipation, of new structured packing shapes.