In this paper, we conduct a systematic analysis to show that existing CPU optimizations targeting scientific/server workloads are not always well suited for mobile apps. In particular, we observe that the well-known and very important concept of identifying and accelerating individual critical instructions in workloads such as SPEC, are not as effective for mobile apps. Several differences in mobile app characteristics including (i) dependencies between critical instructions interspersed with non-critical instructions in the dependence chain, (ii) temporal proximity of the critical instructions in the dynamic stream, and (iii) the bottleneck shifting to the front from the rear of the datapath pipeline, are key contributors to the ineffectiveness of traditional criticality based optimizations. Instead, we propose the concept of Critical Instruction Chains (CritICs)-which are short, critical and self contained sequences of instructions, for aggregate level optimization. With motivating results, we show that an offline profiler/analysis framework can easily identify these CritICs, and we propose a very simple software mechanism in the compiler that exploits ARM's 16-bit ISA format to nearly double the fetch bandwidth of these instructions. We have implemented this entire framework-both profiler and compiler passes, and evaluated its effectiveness for 10 popular apps from the Play Store. Experimental evaluations show that our approach is much more effective than two previously studied criticality optimizations, yielding a speedup of 12.65%, and energy savings of 15% in the CPU (translating to a system wide energy savings of 4.6%), requiring very little additional hardware support.