Predictive computational methods have the potential to significantly accelerate the discovery of new materials with targeted properties by guiding the choice of candidate materials for synthesis. Recently, a planar pyrrole-based azaphenacene molecule (pyrido[2,3-b]pyrido[3′,2′:4,5]pyrrolo[3,2-g]indole, 1) was synthesized and shown to have promising properties for charge transport, which relate to stacking of molecules in its crystal structure. Building on our methods for evaluating small-molecule organic semiconductors using crystal structure prediction, we have screened a set of 27 structural isomers of 1 to assess charge mobility in their predicted crystal structures. Machine-learning techniques are used to identify structural classes across the landscapes of all molecules and we find that, despite differences in the arrangement of hydrogen bond functionality, the predicted crystal structures of the molecules studied here can be classified into a small number of packing types. We analyze the predicted property landscapes of the series of molecules and discuss several metrics that can be used to rank the molecules as promising semiconductors. The results suggest several isomers with superior predicted electron mobilities to 1 and suggest two molecules in particular that represent attractive synthetic targets.