Crystal structure prediction has sometimes been seen as computationally demanding and slow, which would be unsuitable for screening purposes within a suitable time scale for material discovery. With advances in sampling methods, making use of efficient, accurate atom-atom force fields and utilizing parallel, high performance computing, this is no longer true for rigid molecules of the size studied here. In this work, we demonstrate the combination of CSP with charge mobility predictions to screen 28 isomeric molecules as potential organic semiconductors. These molecules were hypothesized as structural variants of the recently synthesized 1. We further combined a recently developed machine-learning method to construct, for the first time, a multi-landscape sketch-map to directly compare the crystal packing landscapes of a set of related molecules to reveal common structural classes among the landscapes and to correlate these classes with crystal properties.