Isomer networks provide a mechanism to understand and interpret relationships between organic molecules with applications in medicinal chemistry and drug design. The extraction of isomer networks is a time- and data-intensive computation (e.g., we have experimentally determined the space required for the computation of a set of 25 isomers of nicotine to be 205 MB; extrapolating this, we have projected the computation to require 8 TB of storage for a set of 1 050 219 isomers of nicotine). In this paper we describe our efforts to improve the network extraction process by using the symmetry present in most molecules to reduce runtime and memory and streamlining the algorithm used for the detection of duplicate dnNames. Together, these techniques result in reductions in memory of up to 60% and improvements in runtime of up to a factor of 100.