  Improving charge carrier mobilities in organic semiconductors is a
challenging task that has hitherto primarily been tackled by empirical
structural tuning of promising core compounds. Knowledge-based methods can
greatly accelerate such local exploration, while a systematic analysis of large
chemical databases can point towards promising design strategies. Here, we
demonstrate such data mining by clustering an in-house database of >64.000
organic molecular crystals for which two charge-transport descriptors, the
electronic coupling and the reorganization energy, have been calculated from
first principles. The clustering is performed according to the Bemis-Murcko
scaffolds of the constituting molecules and according to the sidegroups with
which these molecular backbones are functionalized. In both cases, we obtain
statistically significant structure-property relationships with certain
scaffolds (sidegroups) consistently leading to favorable charge-transport
properties. Functionalizing promising scaffolds with favorable sidegroups
results in engineered molecular crystals for which we indeed compute improved
charge-transport properties.
