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Recent work has shown that computational substrates made from carbon nanotube/polymer mixtures can form trainable Reservoir Computers. This new reservoir computing platform uses computer based evolutionary algorithms to optimise a set of electrical control signals to induce reservoir properties within the substrate. In the training process, evolution decides the value of analogue control signals (voltages) and the location of inputs and outputs on the substrate that improve the performance of the subsequently trained reservoir readout. Here, we evaluate the performance of evolutionary search compared to randomly assigned electrical configurations. The substrate is trained and evaluated on time-series prediction using the Santa Fe Laser generated competition data (dataset A). In addition to the main investigation, we introduce two new features closely linked to the traditional reservoir computing architecture …