The discovery of functional molecular materials with targeted properties is hindered by the challenge of predicting the crystal structure that a given molecule will adopt, as well as the often complex relationship between crystal structure and the relevant properties. Computational approaches for guiding materials discovery have attracted significant attention as a means of identifying the most promising synthetic targets and, therefore, reducing experimental effort spent on materials with poor properties. The reliability and scope of methods for computer-guided material discovery have benefited from recent theoretical advances in configurational space sampling , force field and quantum mechanical methods. Developments in these areas mean that not only wider ranges of atomic configurations can be efficiently sampled, but that their physical and chemical properties can also be confidently predicted prior to laboratory synthesis and characterization. In particular, there has been rapid development in the field of crystal structure prediction (CSP) for organic molecules, as exemplified by the latest blind tests in this area. An aim of CSP methods is to produce the full set of stable (low energy) crystal structures available to a molecule and any given molecule usually leads to a large number of possible structures within a small energy range from the global energy minimum. A recent advance in the use of computational methods to guide the discovery of functional materials is the mapping of simulated properties onto the crystal energy landscape produced by CSP methods. The result, which we refer to as the energy-structure-function (ESF) map of a molecule, describes the possible crystal structures, their relative stabilities and expected properties. The assessment of ESF maps of candidate molecules for a targeted function can help rule out molecules with no (or only high energy) predicted structures with the required properties and prioritize the synthesis of molecules with promising predicted structures. The approach has successfully led to the discovery, crystallization and characterization of a porous organic crystal with exceptionally high porosity and methane storage capacity. ESF maps can be produced for any functional material where the relevant property is calculable from the crystal structure. For example, we proposed their use for the assessment of molecules as potential organic semiconductors. To illustrate this, we assessed a series of aza-substituted pentacenes where the targeted property is the electron mobility, which we calculated using Marcus theory. ESF charge mobility maps have also been reported recently for racemic and enantiomerically pure crystal structures of [6]helicene. Because molecular crystals are held together by many weak, competing non-covalent interactions and the possible crystal packing arrangements are typically close in energy, a subtle change in molecular structure can result in completely different molecular arrangements in the low en-ergy crystal structures available to a molecule. In turn, the strong dependence of charge mobilities on relative molecular positions and orientations means that the mobility in the crystal structures of closely related molecules can be very different. We previously proposed several approaches for ranking hypothetical molecules by the likelihood that they will lead to a high mobility crystal structure. One option is to use the predicted mobility of the most probable (lowest energy) predicted crystal structure of each molecule; another choice is the highest mobility among the low energy predicted crystal structures, and a third suggestion was a landscape-averaged mobility that takes into account the distribution of properties among the low energy structures. We believe that this is an advance on previous computational screening methods for molecular organic photovoltaic applications, in which the important influence of crystal packing differences between molecules on the final electronic properties has not been considered.